Importing SciPy submodules - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of importing and using scipy.linalg

What is the output of this code snippet?

SciPy

from scipy import linalg
import numpy as np

matrix = np.array([[1, 2], [3, 4]])
det = linalg.det(matrix)
print(round(det, 2))

A 5.0

B 2.0

C 0.9999999999999998

D -2.0

💡 Hint

Recall the formula for determinant of a 2x2 matrix [[a,b],[c,d]] is ad - bc.

The determinant of the matrix [[1,2],[3,4]] is (1*4) - (2*3) = 4 - 6 = -2.0. The scipy.linalg.det function computes this value.

❓ data_output

intermediate

Shape of array returned by scipy.fftpack.fft

What is the shape of the array returned by this code?

SciPy

from scipy.fftpack import fft
import numpy as np

arr = np.array([1, 2, 3, 4])
result = fft(arr)
print(result.shape)

A (4,)

B (2, 2)

C (3,)

D (1, 4)

💡 Hint

The FFT output length matches the input length.

The fft function returns a complex array of the same length as the input array. Here, input length is 4, so output shape is (4,).

🔧 Debug

advanced

Identify the error when importing scipy.optimize

What error will this code raise?

SciPy

import scipy
result = scipy.optimize.minimize(lambda x: x**2, 0)

A TypeError: 'function' object is not callable

B AttributeError: module 'scipy' has no attribute 'optimize'

C NameError: name 'minimize' is not defined

D No error, returns optimization result

💡 Hint

Check if submodules are automatically imported with 'import scipy'.

Importing only 'scipy' does not automatically import submodules like 'optimize'. Accessing scipy.optimize without importing it causes AttributeError.

🧠 Conceptual

advanced

Best practice for importing SciPy submodules

Which option is the best practice to import and use the 'integrate' submodule of SciPy for numerical integration?

A

from scipy.integrate import quad
result = quad(lambda x: x, 0, 1)

B

import scipy
result = scipy.integrate.quad(lambda x: x, 0, 1)

C

import integrate
result = integrate.quad(lambda x: x, 0, 1)

D

from scipy import quad
result = quad(lambda x: x, 0, 1)

💡 Hint

Consider which import statement correctly accesses the 'quad' function.

Option A correctly imports 'quad' from 'scipy.integrate' submodule. Option A fails because 'scipy.integrate' is not imported by default. Option A fails because 'integrate' is not a standalone module. Option A fails because 'quad' is not directly in 'scipy'.

🚀 Application

expert

Predict output shape after importing and using scipy.sparse

What is the shape and type of the output after running this code?

SciPy

from scipy.sparse import csr_matrix
import numpy as np

arr = np.array([[0, 0, 1], [1, 0, 0], [0, 2, 0]])
sparse_mat = csr_matrix(arr)
print(sparse_mat.shape, type(sparse_mat))

A (3, 3) <class 'scipy.sparse.csc.csc_matrix'>

B (3, 3) <class 'numpy.ndarray'>

C (3, 3) <class 'scipy.sparse.csr.csr_matrix'>

D (3, 3) <class 'list'>

💡 Hint

csr_matrix creates a compressed sparse row matrix with the same shape as input.

The csr_matrix constructor converts the numpy array into a sparse matrix of type 'csr_matrix' with the same shape (3,3).